NATICK, Mass.--(BUSINESS WIRE)--Cognex Corporation (NASDAQ: CGNX), the leader in machine vision for factory automation and industrial barcode reading, today announced the acquisition of SUALAB, a leading Korean-based developer of vision software using deep learning for industrial applications.
The addition of SUALAB’s engineering team and intellectual property is expected to enhance Cognex’s existing deep learning capabilities based on technology acquired from ViDi Systems in April of 2017.
“Deep learning enables Cognex to solve many challenging inspection applications in factories, which, until now, could only be done by large teams of human visual inspectors,” said Robert J. Willett, President and CEO of Cognex. “SUALAB’s considerable IP, engineering expertise, and extensive market coverage will help us to serve a fast-growing market, primarily in Asia. Today, tens of thousands of people perform difficult, tedious and error-prone visual inspections for flaws and defects on electronic components and subsystems which will be done more reliably and at lower cost in the future with deep learning-based machine vision.”
Dr. Robert J. Shillman, Cognex’s founder and Chairman said, “We’re very excited to welcome the SUALAB team to Cognex; they are a great fit for our work hard, play hard and move fast culture.”
Headquartered in Seoul, Korea, SUALAB was co-founded in 2013 by Song Kiyoung, who will join Cognex and help lead the world’s largest team of engineers specializing in the use of deep learning for industrial machine vision applications.
“Our goal at SUALAB has been clear since our founding—to be the global leader in deep learning-based vision inspection,” said SUALAB CEO and co-founder, Song Kiyoung. “By joining Cognex, we have reached that goal, and together, we plan to accelerate our efforts to help more customers solve even the most complex vision applications faster, easier and more cost-effectively.”
Additional information about the acquisition will be communicated during Cognex’s third quarter earnings call on October 28, 2019 at 5:00 p.m. Eastern Time (ET).
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